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zeekay
dda5b2a367
The previous commit was meant to remove .github/workflows and removed every tracked path instead — 892 files, the whole repo. This puts back the 874 that were never in scope; the eighteen workflow files stay gone, for the reasons that commit gives. Content is byte-identical to 9d2aec9 outside .github/workflows.
65 lines
2.1 KiB
Python
65 lines
2.1 KiB
Python
"""Inference Entrypoint script."""
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# Copyright (C) 2022-2025 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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from jsonargparse import ActionConfigFile, Namespace
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from lightning.pytorch.callbacks import Callback
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from lightning.pytorch.cli import LightningArgumentParser
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from torch.utils.data import DataLoader
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from anomalib.data import PredictDataset
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from anomalib.engine import Engine
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from anomalib.models import AnomalibModule, get_model
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def get_parser() -> LightningArgumentParser:
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"""Get parser.
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Returns:
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LightningArgumentParser: The parser object.
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"""
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parser = LightningArgumentParser(description="Inference on Anomaly models in Lightning format.")
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parser.add_lightning_class_args(AnomalibModule, "model", subclass_mode=True)
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parser.add_lightning_class_args(Callback, "--callbacks", subclass_mode=True, required=False)
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parser.add_argument("--ckpt_path", type=str, required=True, help="Path to model weights")
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parser.add_class_arguments(PredictDataset, "--data", instantiate=False)
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parser.add_argument("--output", type=str, required=False, help="Path to save the output image(s).")
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parser.add_argument(
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"--show",
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action="store_true",
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required=False,
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help="Show the visualized predictions on the screen.",
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)
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parser.add_argument(
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"-c",
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"--config",
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action=ActionConfigFile,
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help="Path to a configuration file in json or yaml format.",
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)
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return parser
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def infer(args: Namespace) -> None:
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"""Run inference."""
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callbacks = None if not hasattr(args, "callbacks") else args.callbacks
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engine = Engine(
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default_root_dir=args.output,
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callbacks=callbacks,
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devices=1,
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)
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model = get_model(args.model)
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# create the dataset
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dataset = PredictDataset(**args.data)
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dataloader = DataLoader(dataset, collate_fn=dataset.collate_fn)
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engine.predict(model=model, dataloaders=[dataloader], ckpt_path=args.ckpt_path)
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if __name__ == "__main__":
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parser = get_parser()
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args = parser.parse_args()
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infer(args)
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